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Project Summary

certainty contains MATLAB code for calculating objective function values at discrete parameter values, finding certainty levels, running Hessian and Jacobian calculations, and quantifying certainty for best-fit results in an n-parameter nonlinear model.

Usage

Prequisits

• MATLAB R2018b or later
• Statistics and Machine Learning Toolbox
• Symbolic Math Toolbox

Example Codes

This repository includes several example codes to demonstrate various models and solutions. Below is a list of the available example codes:

• DEMO_confidence_bound_fluid_Ellis_model.m
• DEMO_confidence_bound_fluid_Powerlaw_model.m
• DEMO_confidence_bound_hyperelastic.m
• DEMO_confidence_bound_solution_conductivity_model.m

Feel free to explore these examples to understand how to apply the models and analyze the results.

Running the Code

To run an example, open MATLAB and navigate to the directory containing the example code. Then, execute for example the script:

run('DEMO_confidence_bound_fluid_Ellis_model.m')

Citing the Project

If you use or modify any examples in your work you should cite the following paper:

Ashkenazi and Solav, (2025). Parameter certainty quantification in nonlinear models. International Journal of Engineering Science, 206, 104163. https://doi.org/10.1016/j.ijengsci.2024.104163